Run splicing cluster assignment, splicing burden, gsva, CLK1

Purpose:

Runs survival analysis models using splicing cluster assignment and 1) single exon splicing burden index (SBI) 2) KEGG Spliceosome GSVA scores or 3) CLK1 exon 4 TPM as a predictor

Usage

Uses a wrapper function (survival_analysis) from utils folder.

Setup

Packages and functions

Load packages, set directory paths and call setup script

library(tidyverse)
library(survival)
library(ggpubr)
library(ggplot2)
library(patchwork)

root_dir <- rprojroot::find_root(rprojroot::has_dir(\.git\))

data_dir <- file.path(root_dir, \data\)
analysis_dir <- file.path(root_dir, \analyses\, \survival\)
input_dir <- file.path(analysis_dir, \results\)
results_dir <- file.path(analysis_dir, \results\)
plot_dir <- file.path(analysis_dir, \plots\)

# If the input and results directories do not exist, create it
if (!dir.exists(results_dir)) {
  dir.create(results_dir, recursive = TRUE)
}

source(file.path(analysis_dir, \util\, \survival_models.R\))
knitr::opts_chunk$set(cache = FALSE)

Set metadata and cluster assignment file paths

metadata_file <- file.path(input_dir, \splicing_indices_with_survival.tsv\)

cluster_file <- file.path(root_dir, \analyses\,
                          \sample-psi-clustering\, \results\,
                          \sample-cluster-metadata-top-5000-events-stranded.tsv\)

kegg_scores_stranded_file <- file.path(root_dir, \analyses\,
                          \sample-psi-clustering\, \results\,
                          \gsva_output_stranded.tsv\)

tpm_file <- file.path(data_dir, \rna-isoform-expression-rsem-tpm.rds\)
clk1_psi_file <- file.path(root_dir, 
                           \analyses\, 
                           \CLK1-splicing_correlations\, 
                           \results\, 
                           \clk1-exon4-psi.tsv\)

Wrangle data Add cluster assignment and spliceosome gsva scores to metadata and define column lgg_group (LGG or non_LGG)

metadata <- read_tsv(metadata_file)
clusters <- read_tsv(cluster_file) %>%
  dplyr::rename(Kids_First_Biospecimen_ID = sample_id)
clk1_psi <- read_tsv(clk1_psi_file) %>%
  dplyr::rename(CLK1_ex4_PSI = PSI) %>%
  select(-plot_group)
gsva_scores <- read_tsv(kegg_scores_stranded_file) %>%
  dplyr::filter(geneset == \KEGG_SPLICEOSOME\) %>%
  dplyr::rename(spliceosome_gsva_score = score)
all_clk4_transcr_counts <- readRDS(tpm_file) %>%
  filter(grepl(\^CLK1\, gene_symbol)) %>%
  mutate(
    transcript_id = case_when(
      transcript_id %in% c(\ENST00000321356.9\, \ENST00000434813.3\, \ENST00000409403.6\) ~ \Exon 4\,
      # transcript_id == \ENST00000321356.9\ ~ \Exon 4\,
      TRUE ~ \Other\
    )
  ) %>%
  group_by(transcript_id) %>%
  summarise(across(starts_with(\BS\), sum, na.rm = TRUE)) %>%
  pivot_longer(cols = -transcript_id, names_to = \Kids_First_Biospecimen_ID\, values_to = \CLK1_Ex4_TPM\) %>%
  filter(transcript_id == \Exon 4\) %>%
  inner_join(clusters, by = \Kids_First_Biospecimen_ID\) %>%
  left_join(clk1_psi)
# how many clusters?
n_clust <- length(unique(clusters$cluster))

metadata <- metadata %>%
  right_join(all_clk4_transcr_counts %>% dplyr::select(Kids_First_Biospecimen_ID,
                                       cluster, CLK1_Ex4_TPM, CLK1_ex4_PSI)) %>%
  left_join(gsva_scores %>% dplyr::select(sample_id,
                                          spliceosome_gsva_score),
            by = c(\Kids_First_Biospecimen_ID\ = \sample_id\)) %>% 
  dplyr::mutate(cluster = glue::glue(\Cluster {cluster}\)) %>%
  # dplyr::mutate(cluster = fct_relevel(cluster,
  #                                             paste0(\Cluster \, 1:n_clust))) %>%
  dplyr::mutate(cluster = forcats::fct_relevel(cluster, \Cluster 6\, after = 0)) %>%
  dplyr::mutate(lgg_group = case_when(
    plot_group == \Low-grade glioma\ ~ \LGG\,
    TRUE ~ \non-LGG\
  )) %>%
  dplyr::mutate(SBI = SI_Total * 10) %>%
  dplyr::mutate(age_at_diagnosis_years = age_at_diagnosis_days/365.25)

Generate coxph models including extent of tumor resection, lgg group, cluster assignment, SBI, and CLK1 exon 4 TPM as covariates

add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                              terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI+CLK1_Ex4_TPM\,
                               file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS\),
                               \multivariate\,
                               years_col = \OS_years\,
                               status_col = \OS_status\)

forest_os <- plotForest(readRDS(file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS\)))

forest_os

ggsave(file.path(plot_dir, \forest_add_OS_resection_lgg_group_cluster_assignment_SBI_CLK1_Ex4_TPM.pdf\),
       forest_os,
       width = 10, height = 6, units = \in\,
       device = \pdf\)

add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                              terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+SBI+CLK1_Ex4_TPM\,
                               file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS\),
                               \multivariate\,
                               years_col = \EFS_years\,
                               status_col = \EFS_status\)

forest_efs <- plotForest(readRDS(file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_SBI_CLK1_Ex4_TPM.RDS\)))

forest_efs

ggsave(file.path(plot_dir, \forest_add_EFS_resection_lgg_group_cluster_assignment_SBI_CLK1_Ex4_TPM.pdf\),
       forest_efs,
       width = 10, height = 6, units = \in\,
       device = \pdf\)

repeat analysis with CLK1 exon 4 TPM alone

add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                              terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_Ex4_TPM\,
                               file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS\),
                               \multivariate\,
                               years_col = \OS_years\,
                               status_col = \OS_status\)

forest_os <- plotForest(readRDS(file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS\)))

forest_os

ggsave(file.path(plot_dir, \forest_add_OS_resection_lgg_group_cluster_assignment_CLK1_Ex4_TPM.pdf\),
       forest_os,
       width = 10, height = 6, units = \in\,
       device = \pdf\)


add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                              terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_Ex4_TPM\,
                               file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS\),
                               \multivariate\,
                               years_col = \EFS_years\,
                               status_col = \EFS_status\)

forest_efs <- plotForest(readRDS(file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM.RDS\)))

forest_efs

ggsave(file.path(plot_dir, \forest_add_EFS_resection_lgg_group_cluster_assignment_CLK1_Ex4_TPM.pdf\),
       forest_efs,
       width = 10, height = 6, units = \in\,
       device = \pdf\)

repeat analysis with CLK1 exon 4 PSI

add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                              terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_ex4_PSI\,
                               file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS\),
                               \multivariate\,
                               years_col = \OS_years\,
                               status_col = \OS_status\)

forest_os <- plotForest(readRDS(file.path(results_dir, \cox_OS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS\)))

forest_os

ggsave(file.path(plot_dir, \forest_add_OS_resection_lgg_group_cluster_assignment_CLK1_ex4_PSI.pdf\),
       forest_os,
       width = 10, height = 6, units = \in\,
       device = \pdf\)

add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c(\Not Reported\, \Unavailable\),],
                              terms = \extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+CLK1_ex4_PSI\,
                               file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS\),
                               \multivariate\,
                               years_col = \EFS_years\,
                               status_col = \EFS_status\)

forest_efs <- plotForest(readRDS(file.path(results_dir, \cox_EFS_additive_terms_resection_lgg_group_cluster_CLK1_ex4_PSI.RDS\)))

forest_efs

ggsave(file.path(plot_dir, \forest_add_EFS_resection_lgg_group_cluster_assignment_CLK1_ex4_PSI.pdf\),
       forest_efs,
       width = 10, height = 6, units = \in\,
       device = \pdf\)

Interaction with GSVA, SBI, CLK1

models <- c("spliceosome_gsva_score", "SBI", "CLK1_Ex4_TPM", "CLK1_ex4_PSI")
# by cluster
for (each in models) {
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster*", each, "+age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("forest_int_EFS_resection_lgg_group_cluster_assignment_", each, ".pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")

  int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster*", each, "+age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "OS_years",
                                 status_col = "OS_status")
  
  int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_", each, ".RDS"))))
  
  int_forest_os
  
  ggsave(file.path(plot_dir, paste0("forest_int_OS_resection_lgg_group_cluster_assignment_", each, ".pdf")),
         int_forest_os,
         width = 10, height = 6, units = "in",
         device = "pdf")
}

## clk1 x age
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster+CLK1_Ex4_TPM*age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM_age.RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_CLK1_Ex4_TPM_age.RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("forest_int_EFS_resection_lgg_group_cluster_clk1_age.pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")

  int_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster+CLK1_Ex4_TPM*age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_clk1_age.RDS")),
                                 "multivariate",
                                 years_col = "OS_years",
                                 status_col = "OS_status")
  
  int_forest_os <- plotForest(readRDS(file.path(results_dir, paste0("cox_OS_interaction_terms_resection_lgg_group_cluster_clk1_age.RDS"))))
  
  int_forest_os
  
  ggsave(file.path(plot_dir, paste0("forest_int_OS_resection_lgg_group_cluster_clk1_age.pdf")),
         int_forest_os,
         width = 10, height = 6, units = "in",
         device = "pdf")

models2 <- c("SBI", "CLK1_Ex4_TPM", "CLK1_ex4_PSI")
for (each in models2) {
  #### by spliceosome_gsva_score
  int_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                                terms = paste0("extent_of_tumor_resection+lgg_group+cluster+spliceosome_gsva_score*", each, "+age_at_diagnosis_years"),
                                 file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".RDS")),
                                 "multivariate",
                                 years_col = "EFS_years",
                                 status_col = "EFS_status")
  
  int_forest_efs <- plotForest(readRDS(file.path(results_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".RDS"))))
  
  int_forest_efs
  
  ggsave(file.path(plot_dir, paste0("cox_EFS_interaction_terms_resection_lgg_group_cluster_spliceosome_gsva_score_", each, ".pdf")),
         int_forest_efs,
         width = 10, height = 6, units = "in",
         device = "pdf")
}
  


add_model_efs <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_efs <- plotForest(readRDS(file.path(results_dir, "cox_EFS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS")))

forest_efs

ggsave(file.path(plot_dir, "forest_add_EFS_resection_lgg_group_cluster_assignment_spliceosome_score_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")


add_model_os <- fit_save_model(metadata[!metadata$extent_of_tumor_resection %in% c("Not Reported", "Unavailable"),],
                              terms = "extent_of_tumor_resection+lgg_group+cluster+age_at_diagnosis_years+spliceosome_gsva_score+CLK1_Ex4_TPM",
                               file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS"),
                               "multivariate",
                               years_col = "EFS_years",
                               status_col = "EFS_status")

forest_os <- plotForest(readRDS(file.path(results_dir, "cox_OS_additive_terms_resection_lgg_group_cluster_spliceosome_score_CLK1_Ex4_TPM.RDS")))

forest_os

ggsave(file.path(plot_dir, "forest_add_OS_resection_lgg_group_cluster_assignment_spliceosome_score_CLK1_Ex4_TPM.pdf"),
       forest_efs,
       width = 10, height = 6, units = "in",
       device = "pdf")

Filter for clusters

cluster_list <- unique(metadata$cluster)

for (each in cluster_list) {
cluster_df <- metadata %>%
  dplyr::filter(cluster == each,
                !is.na(EFS_days)) %>%
  dplyr::mutate(CLK1_TPM_group = case_when(
      CLK1_Ex4_TPM > summary(CLK1_Ex4_TPM)[\3rd Qu.\] ~ \High CLK1 TPM\,
      CLK1_Ex4_TPM < summary(CLK1_Ex4_TPM)[\1st Qu.\] ~ \Low CLK1 TPM\,
      TRUE ~ NA_character_),
      CLK1_PSI_group = case_when(CLK1_ex4_PSI > summary(CLK1_ex4_PSI)[\3rd Qu.\] ~ \High CLK1 PSI\,
      CLK1_ex4_PSI < summary(CLK1_ex4_PSI)[\1st Qu.\] ~ \Low CLK1 PSI\,
      TRUE ~ NA_character_
    )) %>%
  dplyr::mutate(CLK1_TPM_group = fct_relevel(CLK1_TPM_group,
                                                 c(\Low CLK1 TPM\, \High CLK1 TPM\)),
                CLK1_PSI_group = fct_relevel(CLK1_PSI_group,
                                                 c(\Low CLK1 PSI\, \High CLK1 PSI\))) %>%
 # collapse groups which do not have min N
   dplyr::mutate(
    plot_group = forcats::fct_drop(plot_group),
    plot_group = forcats::fct_lump_min(plot_group, min = 3, other_level = \Collapsed\)
  )

 safe_each <- gsub(\[^A-Za-z0-9_-]+\, \_\, each)

# Generate KM models with `CLK1_TPM_group` as covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
cluster_clk_tpm_kap_os <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_TPM_group)),
  ind_var = \CLK1_TPM_group\,
  test = \kap.meier\,
  metadata_sample_col = \Kids_First_Biospecimen_ID\,
  days_col = \OS_days\,
  status_col = \OS_status\
)

readr::write_rds(cluster_clk_tpm_kap_os,
                 file.path(results_dir, paste0( \logrank_\, safe_each, \_OS_clk1_tpm_group.RDS\)))

cluster_clk_tpm_kap_efs <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_TPM_group)),
  ind_var = \CLK1_TPM_group\,
  test = \kap.meier\,
  metadata_sample_col = \Kids_First_Biospecimen_ID\,
  days_col = \EFS_days\,
  status_col = \EFS_status\
)

readr::write_rds(cluster_clk_tpm_kap_efs,
                 file.path(results_dir, paste0( \logrank_\, safe_each, \_EFS_clk1_tpm_group.RDS\)))

# Generate KM models with `CLK1_PSI_group` as covariate
# Generate kaplan meier survival models for OS and EFS, and save outputs
cluster_clk_psi_kap_os <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_PSI_group)),
  ind_var = \CLK1_PSI_group\,
  test = \kap.meier\,
  metadata_sample_col = \Kids_First_Biospecimen_ID\,
  days_col = \OS_days\,
  status_col = \OS_status\
)

readr::write_rds(cluster_clk_psi_kap_os,
                 file.path(results_dir, paste0( \logrank_\, safe_each, \_OS_clk1_psi_group.RDS\)))

cluster_clk_psi_kap_efs <- survival_analysis(
  metadata  = cluster_df %>% 
    dplyr::filter(!is.na(CLK1_PSI_group)),
  ind_var = \CLK1_PSI_group\,
  test = \kap.meier\,
  metadata_sample_col = \Kids_First_Biospecimen_ID\,
  days_col = \EFS_days\,
  status_col = \EFS_status\
)

readr::write_rds(cluster_clk_psi_kap_efs,
                 file.path(results_dir, paste0( \logrank_\, safe_each, \_EFS_clk1_psi_group.RDS\)))

# Generate cluster KM SI_group plots

km_cluster_clk_tpm_os_plot <- plotKM(model = cluster_clk_tpm_kap_os,
                    variable = \CLK1_TPM_group\,
                    combined = F, 
                    title = paste0(each, \

Print session Info

sessionInfo()

R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gtools_3.9.5    survminer_0.4.9 patchwork_1.2.0 ggpubr_0.6.0   
 [5] survival_3.7-0  lubridate_1.9.4 forcats_1.0.1   stringr_1.6.0  
 [9] dplyr_1.1.4     purrr_1.2.0     readr_2.1.6     tidyr_1.3.1    
[13] tibble_3.3.0    ggplot2_4.0.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.54          bslib_0.9.0        rstatix_0.7.2     
 [5] lattice_0.22-7     tzdb_0.5.0         vctrs_0.6.5        tools_4.4.0       
 [9] generics_0.1.4     parallel_4.4.0     pkgconfig_2.0.3    Matrix_1.7-4      
[13] data.table_1.17.8  RColorBrewer_1.1-3 S7_0.2.1           lifecycle_1.0.4   
[17] compiler_4.4.0     farver_2.1.2       textshaping_1.0.4  carData_3.0-5     
[21] colorblindr_0.1.0  htmltools_0.5.8.1  sass_0.4.10        yaml_2.3.10       
[25] crayon_1.5.3       pillar_1.11.1      car_3.1-2          jquerylib_0.1.4   
[29] cachem_1.1.0       abind_1.4-5        km.ci_0.5-6        commonmark_2.0.0  
[33] tidyselect_1.2.1   digest_0.6.39      stringi_1.8.7      labeling_0.4.3    
[37] splines_4.4.0      cowplot_1.1.3      rprojroot_2.1.1    fastmap_1.2.0     
[41] grid_4.4.0         colorspace_2.1-2   cli_3.6.5          magrittr_2.0.4    
[45] broom_1.0.10       withr_3.0.2        scales_1.4.0       backports_1.5.0   
[49] bit64_4.6.0-1      timechange_0.3.0   rmarkdown_2.30     ggtext_0.1.2      
[53] bit_4.6.0          gridExtra_2.3      ggsignif_0.6.4     ragg_1.5.0        
[57] zoo_1.8-12         hms_1.1.4          evaluate_1.0.5     knitr_1.50        
[61] KMsurv_0.1-5       markdown_1.13      survMisc_0.5.6     rlang_1.1.6       
[65] Rcpp_1.1.0         gridtext_0.1.5     xtable_1.8-4       glue_1.8.0        
[69] xml2_1.5.0         vroom_1.6.6        jsonlite_2.0.0     R6_2.6.1          
[73] systemfonts_1.3.1